Intelligent urban transportation systems are becoming increasingly important for commuters in cities. Such systems, for example, enable city-dwellers to quickly identify an optimal route in a transit network between a specified origin and destination when planning trips. In many aspects, route planning is an important part of intelligent transportation systems.
Traditional route planners of public transportation such as buses follow a fixed or static schedule. However, in a realistic transit network, buses' arrival times are not as accurate as scheduled since they are largely dependent on real-time traffic situations. In fact, the travel time in an urban traffic environment is highly stochastic and time-dependent. Hence, the results returned by static route planners are often inadequate in real world and cause user dissatisfaction.
Additionally, existing route planners have several drawbacks. First, the query result is the same no matter whether the departure time falls in peak or off-peak periods. Next, the travel time estimated by the route planners is not accurate. For example, Google Maps returns a travel time of about 30 minutes for a journey consisting of 30 bus stops. In practice, the journey takes longer, for example, at least 50 minutes due to various traffic conditions at each stop.